{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "815b7181-56f8-4cf1-a370-1459bd8d50d9",
   "metadata": {
    "id": "815b7181-56f8-4cf1-a370-1459bd8d50d9"
   },
   "source": [
    "Using Ragas for evaluation: https://docs.ragas.io/en/latest/getstarted/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16e0ad45-f5b2-4e57-bdc6-2a808a315d24",
   "metadata": {
    "id": "16e0ad45-f5b2-4e57-bdc6-2a808a315d24",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%pip install langchain_community\n",
    "%pip install langchain_experimental\n",
    "%pip install langchain-openai\n",
    "%pip install langchainhub\n",
    "%pip install chromadb\n",
    "%pip install langchain\n",
    "%pip install python-dotenv\n",
    "%pip uninstall uvloop -y\n",
    "%pip install PyPDF2 -q --user\n",
    "%pip install rank_bm25\n",
    "\n",
    "# new\n",
    "%pip install ragas\n",
    "%pip install tqdm -q --user\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f884314f-870c-4bfb-b6c1-a5b4801ec172",
   "metadata": {
    "executionInfo": {
     "elapsed": 5873,
     "status": "ok",
     "timestamp": 1718829482149,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "f884314f-870c-4bfb-b6c1-a5b4801ec172"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import openai\n",
    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
    "from langchain import hub\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "import chromadb\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_core.runnables import RunnableParallel\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "from PyPDF2 import PdfReader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.docstore.document import Document\n",
    "from langchain_community.retrievers import BM25Retriever\n",
    "from langchain.retrievers import EnsembleRetriever\n",
    "\n",
    "## new\n",
    "import tqdm as notebook_tqdm\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datasets import Dataset\n",
    "from ragas import evaluate\n",
    "from ragas.testset.generator import TestsetGenerator\n",
    "from ragas.testset.evolutions import simple, reasoning, multi_context\n",
    "from ragas.metrics import (\n",
    "    answer_relevancy,\n",
    "    faithfulness,\n",
    "    context_recall,\n",
    "    context_precision,\n",
    "    answer_correctness,\n",
    "    answer_similarity\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "wKaTaVA0Ixtg",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1718829482149,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "wKaTaVA0Ixtg"
   },
   "outputs": [],
   "source": [
    "# variables\n",
    "_ = load_dotenv(dotenv_path='env.txt')\n",
    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')\n",
    "openai.api_key = os.environ['OPENAI_API_KEY']\n",
    "embedding_function = OpenAIEmbeddings()\n",
    "llm = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0)\n",
    "pdf_path = \"google-2023-environmental-report.pdf\"\n",
    "collection_name = \"google_environmental_report\"\n",
    "str_output_parser = StrOutputParser()\n",
    "user_query = \"What are Google's environmental initiatives?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "Tz7M7KSBl1y3",
   "metadata": {
    "executionInfo": {
     "elapsed": 962,
     "status": "ok",
     "timestamp": 1718829483109,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "Tz7M7KSBl1y3"
   },
   "outputs": [],
   "source": [
    "# LLMs/Embeddings\n",
    "embedding_ada = \"text-embedding-ada-002\"\n",
    "model_gpt35=\"gpt-3.5-turbo\"\n",
    "model_gpt4=\"gpt-4o-mini\"\n",
    "\n",
    "embedding_function = OpenAIEmbeddings(model=embedding_ada, openai_api_key=openai.api_key)\n",
    "llm = ChatOpenAI(model=model_gpt35, openai_api_key=openai.api_key, temperature=0.0)\n",
    "generator_llm = ChatOpenAI(model=model_gpt35, openai_api_key=openai.api_key, temperature=0.0)\n",
    "critic_llm = ChatOpenAI(model=model_gpt4, openai_api_key=openai.api_key, temperature=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3ad428a-3eb6-40ec-a1a5-62565ead1e5b",
   "metadata": {
    "id": "d3ad428a-3eb6-40ec-a1a5-62565ead1e5b"
   },
   "outputs": [],
   "source": [
    "#### INDEXING ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98ccda2c-0f4c-41c5-804d-2227cdf35aa7",
   "metadata": {
    "executionInfo": {
     "elapsed": 20404,
     "status": "ok",
     "timestamp": 1718829503911,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "98ccda2c-0f4c-41c5-804d-2227cdf35aa7"
   },
   "outputs": [],
   "source": [
    "# # Load the PDF and extract text\n",
    "pdf_reader = PdfReader(pdf_path)\n",
    "text = \"\"\n",
    "for page in pdf_reader.pages:\n",
    "    text += page.extract_text()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "927a4c65-aa05-486c-8295-2f99673e7c20",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1718829503912,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "927a4c65-aa05-486c-8295-2f99673e7c20"
   },
   "outputs": [],
   "source": [
    "# Split\n",
    "character_splitter = RecursiveCharacterTextSplitter(\n",
    "    separators=[\"\\n\\n\", \"\\n\", \". \", \" \", \"\"],\n",
    "    chunk_size=1000,\n",
    "    chunk_overlap=200\n",
    ")\n",
    "splits = character_splitter.split_text(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "738a9fdf-1e0a-4a52-becc-10995e74937e",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1718829503913,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "738a9fdf-1e0a-4a52-becc-10995e74937e"
   },
   "outputs": [],
   "source": [
    "dense_documents = [Document(page_content=text, metadata={\"id\": str(i), \"source\": \"dense\"}) for i, text in enumerate(splits)]\n",
    "sparse_documents = [Document(page_content=text, metadata={\"id\": str(i), \"source\": \"sparse\"}) for i, text in enumerate(splits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b13568c-d633-464d-8c43-0d55f34cc8c1",
   "metadata": {
    "executionInfo": {
     "elapsed": 8194,
     "status": "ok",
     "timestamp": 1718829543927,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "6b13568c-d633-464d-8c43-0d55f34cc8c1"
   },
   "outputs": [],
   "source": [
    "chroma_client = chromadb.Client()\n",
    "vectorstore = Chroma.from_documents(\n",
    "    documents=dense_documents,\n",
    "    embedding=embedding_function,\n",
    "    collection_name=collection_name,\n",
    "    client=chroma_client\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8cdf0e41-6362-465c-a8de-6af3c51ac5e2",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1718829543927,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "8cdf0e41-6362-465c-a8de-6af3c51ac5e2"
   },
   "outputs": [],
   "source": [
    "dense_retriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n",
    "sparse_retriever = BM25Retriever.from_documents(sparse_documents, k=10)\n",
    "ensemble_retriever = EnsembleRetriever(retrievers=[dense_retriever, sparse_retriever], weights=[0.5, 0.5], c=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ce8df01-925b-45b5-8fb8-17b5c40c581f",
   "metadata": {
    "id": "6ce8df01-925b-45b5-8fb8-17b5c40c581f"
   },
   "outputs": [],
   "source": [
    "#### RETRIEVAL and GENERATION ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb47c817-b5ac-4d90-84ee-4cd209e52a80",
   "metadata": {
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1718829544356,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "eb47c817-b5ac-4d90-84ee-4cd209e52a80"
   },
   "outputs": [],
   "source": [
    "# Prompt\n",
    "prompt = hub.pull(\"jclemens24/rag-prompt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "-gM_jXU-Y4-B",
   "metadata": {
    "executionInfo": {
     "elapsed": 153,
     "status": "ok",
     "timestamp": 1718829545294,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "-gM_jXU-Y4-B"
   },
   "outputs": [],
   "source": [
    "# Relevance check prompt\n",
    "relevance_prompt_template = PromptTemplate.from_template(\n",
    "    \"\"\"\n",
    "    Given the following question and retrieved context, determine if the context is relevant to the question.\n",
    "    Provide a score from 1 to 5, where 1 is not at all relevant and 5 is highly relevant.\n",
    "    Return ONLY the numeric score, without any additional text or explanation.\n",
    "\n",
    "    Question: {question}\n",
    "    Retrieved Context: {retrieved_context}\n",
    "\n",
    "    Relevance Score:\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8975479-b3e3-481d-ad7b-08b4eb3faaef",
   "metadata": {
    "executionInfo": {
     "elapsed": 142,
     "status": "ok",
     "timestamp": 1718829545996,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "e8975479-b3e3-481d-ad7b-08b4eb3faaef"
   },
   "outputs": [],
   "source": [
    "# Post-processing\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd9db713-f705-4b65-800e-2c4e3d0e4ef4",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1718829547716,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "fd9db713-f705-4b65-800e-2c4e3d0e4ef4"
   },
   "outputs": [],
   "source": [
    "def extract_score(llm_output):\n",
    "    try:\n",
    "        score = float(llm_output.strip())\n",
    "        return score\n",
    "    except ValueError:\n",
    "        return 0\n",
    "\n",
    "# Chain it all together with LangChain\n",
    "def conditional_answer(x):\n",
    "    relevance_score = extract_score(x['relevance_score'])\n",
    "    if relevance_score < 4:\n",
    "        return \"I don't know.\"\n",
    "    else:\n",
    "        return x['answer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "Uso1sjKHOCEp",
   "metadata": {
    "executionInfo": {
     "elapsed": 104,
     "status": "ok",
     "timestamp": 1718829549574,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "Uso1sjKHOCEp"
   },
   "outputs": [],
   "source": [
    "rag_chain_from_docs = (\n",
    "    RunnablePassthrough.assign(context=(lambda x: format_docs(x[\"context\"])))\n",
    "    | RunnableParallel(\n",
    "        {\"relevance_score\": (\n",
    "            RunnablePassthrough()\n",
    "            | (lambda x: relevance_prompt_template.format(question=x['question'], retrieved_context=x['context']))\n",
    "            | llm\n",
    "            | str_output_parser\n",
    "        ), \"answer\": (\n",
    "            RunnablePassthrough()\n",
    "            | prompt\n",
    "            | llm\n",
    "            | str_output_parser\n",
    "        )}\n",
    "    )\n",
    "    | RunnablePassthrough().assign(final_answer=conditional_answer)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "qUJs89R6ZNip",
   "metadata": {
    "executionInfo": {
     "elapsed": 194,
     "status": "ok",
     "timestamp": 1718829551391,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "qUJs89R6ZNip"
   },
   "outputs": [],
   "source": [
    "rag_chain_similarity = RunnableParallel(\n",
    "    {\"context\": dense_retriever,\n",
    "     \"question\": RunnablePassthrough()\n",
    "}).assign(answer=rag_chain_from_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "_S6btCZhY-UV",
   "metadata": {
    "executionInfo": {
     "elapsed": 145,
     "status": "ok",
     "timestamp": 1718829552219,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "_S6btCZhY-UV"
   },
   "outputs": [],
   "source": [
    "rag_chain_hybrid = RunnableParallel(\n",
    "    {\"context\": ensemble_retriever,\n",
    "     \"question\": RunnablePassthrough()\n",
    "}).assign(answer=rag_chain_from_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "lTzNd07mZVTA",
   "metadata": {
    "id": "lTzNd07mZVTA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Question to Similarity Search: What are Google's environmental initiatives?\n",
      "\n",
      "Relevance Score: 4\n",
      "\n",
      "Final Answer:\n",
      "Google's environmental initiatives include empowering individuals to take action through sustainability features in products like Google Maps, Google Nest thermostats, and Google Flights. They also work with partners and customers to reduce carbon emissions collectively. Google is a founding member of the iMasons Climate Accord, supports industry-wide change through initiatives like the ReFED Catalytic Grant Fund, and partners with organizations like The Nature Conservancy to support environmental projects. Additionally, Google invests in breakthrough innovation through the Google.org Impact Challenge on Climate Innovation and supports sustainability-focused accelerators for early-stage innovations. Google also aims to protect and enhance nature and biodiversity through its campuses and technology.\n",
      "\n",
      "\n",
      "Retrieved Documents:\n",
      "Document 1: Document ID: 12 source: dense\n",
      "Content:\n",
      "The opportunity we have through our products and \n",
      "platforms is reflected in our updated environmental sustainability strategy, which focuses on where we can make the most significant positive impact. Our work is organized around three key pillars: empowering individuals to take action, working together with our partners and customers, and operating our business sustainably.\n",
      "In 2022, we reached our goal to help 1 billion people \n",
      "make more sustainable choices through our products. We achieved this by offering sustainability features like eco-friendly routing in Google Maps, energy efficiency features in Google Nest thermostats, and carbon emissions information in Google Flights. Looking ahead, our aspiration is to help individuals, cities, and other partners collectively reduce 1 gigaton of their carbon equivalent emissions annually by 2030.\n",
      " 2\n",
      "\n",
      "Document 2: Document ID: 311 source: dense\n",
      "Content:\n",
      "In 2022, we audited a subset of our suppliers to verify \n",
      "compliance for the following environmental criteria: implementation of environmental management systems, environmental permits and reporting, product content restrictions, and resource efficiency, as well as management of hazardous substances, wastewater,  solid waste, and air emissions.\n",
      "Googlers chat among indoor plants at our Pier 57 office in New York City.   79\n",
      "2023 Environmental Report  Public policy and advocacy\n",
      "We know that strong public policy action is critical to \n",
      "creating prosperous, equitable, and resilient low-carbon economies around the world. \n",
      "The United Nations Framework Convention on Climate \n",
      "Change (UNFCCC)’s 2015 Paris Agreement states that humanity must “keep global temperature rise this century well below 2°C above pre-industrial levels.”\n",
      " 143 Google\n",
      "\n",
      "Document 3: Document ID: 344 source: dense\n",
      "Content:\n",
      "iMasons Climate AccordGoogle is a founding member and part of the governing body of the iMasons Climate Accord, a coalition united on carbon reduction in digital infrastructure.\n",
      "ReFEDIn 2022, to activate industry-wide change, Google provided anchor funding to kickstart the ReFED Catalytic Grant Fund, with the goal of accelerating and scaling food waste solutions.\n",
      "The Nature Conservancy (TNC)In 2022, Google supported three of the Nature Conservancy’s watershed projects in Chile and the United States, and Google.org supported a three-phased approach to catalyze active reforestation of kelp at impactful scales. Google.org also provided a grant to TNC to develop a machine-learning-powered timber-tracing API to stop deforestation in the Amazon at scale; a team of Google engineers is working full-time for six months with TNC to develop this product as part of the Google.org Fellowship Program.\n",
      "\n",
      "Document 4: Document ID: 13 source: dense\n",
      "Content:\n",
      "2\n",
      "After two years of condensed reporting, we’re sharing a deeper dive into our approach in one place in our 2023 Environmental Report. In 2022, we continued to make measurable progress in many key ways, such as:\n",
      "• We enhanced and launched new sustainabilityproduct features , such as eco-friendly routing in\n",
      "Maps, which is estimated to have helped preventmore than 1.2 million metric tons of carbon emissionsfrom launch through 2022—equivalent to takingapproximately 250,000 fuel-based cars off the roadfor a year.\n",
      " 3\n",
      "• We expanded the availability of Google EarthEngine —which provides access to reliable, up-to-\n",
      "date insights on how our planet is changing—toinclude businesses and governments worldwide as anenterprise-grade service through Google Cloud.• We opened our new Bay View campus , which is\n",
      "all-electric, net water-positive, restores over 17 acresof high-value nature, and incorporates the leadingprinciples of circular design.\n",
      "\n",
      "Document 5: Document ID: 115 source: dense\n",
      "Content:\n",
      "of over 140 partner organizations.\n",
      "The Google.org Impact Challenge on Climate Innovation supports breakthrough projects that use data and technology to \n",
      "accelerate climate action.\n",
      "The journey ahead\n",
      "From measuring and monitoring changes on the Earth’s surface, improving forecast and prediction models for flooding and wildfires, optimizing operations, combining disparate data sources, and designing more efficient products, we continue to leverage our expertise in technology and apply the latest advancements to help solve global challenges.\n",
      "We believe that by working together with our partners and \n",
      "customers, we can make a real difference in addressing the challenges of climate change and ecosystem degradation. LEARN MORE\n",
      "• Data Commons\n",
      "• Environmental Insights Explorer\n",
      "• Google Cloud sustainability\n",
      "• Google Earth Engine\n",
      "• Sustainability-focused accelerators   31\n",
      "2023 Environmental Report  Operating \n",
      "sustainably\n",
      "We’re showing the way forward \n",
      "through our own operationsOur ambition\n",
      "\n",
      "Document 6: Document ID: 67 source: dense\n",
      "Content:\n",
      "Our approach\n",
      "Supporting partners\n",
      "Investing in breakthrough \n",
      "innovation\n",
      "Creating ecosystems for \n",
      "collaboration\n",
      "The journey ahead\n",
      "   21\n",
      "2023 Environmental Report  Our ambition\n",
      "We believe that Google has a unique \n",
      "opportunity that extends beyond reducing \n",
      "the environmental impacts of our own \n",
      "operations and value chain. By organizing \n",
      "information about our planet and making \n",
      "it actionable through technology and \n",
      "platforms, we can help partners and \n",
      "customers create even more positive impact.\n",
      "Digital technologies  play a critical role in industry \n",
      "transitions, allowing us to measure and track sustainability \n",
      "progress, optimize the use of resources, reduce \n",
      "greenhouse gas emissions, and enable a more circular \n",
      "economy. 50 Cloud computing and digital technologies \n",
      "underpin the transformation in many sectors, such as \n",
      "energy, transportation, and agriculture. Research  that \n",
      "we commissioned in 2022 found that 20%–25% of what’s \n",
      "required for the EU’s 2050 net-zero goal requires some\n",
      "\n",
      "Document 7: Document ID: 346 source: dense\n",
      "Content:\n",
      "World Business Council for Sustainable Development (WBCSD)Google has been a member of the WBCSD for several years and participates in a number of its initiatives. \n",
      "We’re a\n",
      "ctively involved in initiatives related to improving well-being for people and the planet, including  \n",
      "shifting diets, consumer behavior changes, and regenerative agriculture. \n",
      "World Resources Institute (WRI)Google has a 13-year long relationship with WRI for impact-focused collaboration. Some key projects include developing a near-real-time land cover dataset ( Dynamic World ), deforestation monitoring and alerts ( Global \n",
      "Forest Watch ), ending commodity-driven deforestation and accelerating restoration ( Forest Data Partnership ), \n",
      "measuring and mitigating extreme heat ( supported by Google.org ), and educating stakeholders on 24/7 CFE.   84\n",
      "2023 Environmental Report  Awards and recognition\n",
      "2022 CDP Climate Change A List  \n",
      "Alphabet has been named to CDP’s Climate Change A list,\n",
      "\n",
      "Document 8: Document ID: 111 source: dense\n",
      "Content:\n",
      "Googlers collaborate in the Event Center at our Bay View campus.   30\n",
      "2023 Environmental Report  Google for Startups\n",
      "By investing early in technologies aimed at tackling \n",
      "sustainability challenges like climate change, we have the potential to move the needle on sustainability and positively impact our planet. We have a portfolio of sustainability-focused accelerators , which support  \n",
      "early stage innovations to grow and scale.\n",
      "Google for Startups \n",
      "Accelerator\n",
      "Google for Startups is working to identify, support, and\n",
      "\n",
      "Document 9: Document ID: 66 source: dense\n",
      "Content:\n",
      "the United States for pre-owned products, such as used and refurbished products. The journey \n",
      "ahead\n",
      "While a single individual’s actions may seem small, when \n",
      "billions of people have the tools to make more sustainable decisions, they add up to have a meaningful impact on their communities and the entire planet. \n",
      "We’re excited by the opportunity to enable climate and \n",
      "environmental action far beyond Google’s direct impact, through information and innovation.\n",
      "LEARN MORE\n",
      " • Empowering with technology\n",
      " • Google Maps eco-friendly routing\n",
      " • Searching for sustainability with Google\n",
      " • Supporting a clean energy future with Nest Renew\n",
      " • The search for sustainability   20\n",
      "2023 Environmental Report  Working \n",
      "together\n",
      "We’re working together with our \n",
      "partners and customers to advance technology for sustainabilityOur ambition\n",
      "Our approach\n",
      "Supporting partners\n",
      "Investing in breakthrough \n",
      "innovation\n",
      "Creating ecosystems for \n",
      "collaboration\n",
      "The journey ahead\n",
      "   21\n",
      "2023 Environmental Report  Our ambition\n",
      "\n",
      "Document 10: Document ID: 262 source: dense\n",
      "Content:\n",
      "135 \n",
      "Preserving nature is critical both to mitigating climate  \n",
      "change and adapting to it. We want nature and people to flourish together in the communities that Google calls  home, as well as the ecosystems where we source food  for the hundreds of cafes we operate. Our approach\n",
      "We strive to protect and enhance nature and biodiversity through our campuses and technology.\n",
      "Google has offices in nearly 60 countries around the world \n",
      "(as of year-end 2022). In these locations, we aim to protect and enhance nature and biodiversity through a four-pillar approach that starts with building biodiversity at our own office and campus developments, as well as protecting nature and making it more accessible in the surrounding communities where we operate (see Figure 24). \n",
      "Our approach further focuses on sourcing responsibly\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Question - Submitted to the similarity / dense vector search\n",
    "result = rag_chain_similarity.invoke(user_query)\n",
    "retrieved_docs = result['context']\n",
    "\n",
    "print(f\"Original Question to Similarity Search: {user_query}\\n\")\n",
    "print(f\"Relevance Score: {result['answer']['relevance_score']}\\n\")\n",
    "print(f\"Final Answer:\\n{result['answer']['final_answer']}\\n\\n\")\n",
    "print(\"Retrieved Documents:\")\n",
    "for i, doc in enumerate(retrieved_docs, start=1):\n",
    "    print(f\"Document {i}: Document ID: {doc.metadata['id']} source: {doc.metadata['source']}\")\n",
    "    print(f\"Content:\\n{doc.page_content}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b30177a-f9ab-45e4-812d-33b0f97325bd",
   "metadata": {
    "id": "8b30177a-f9ab-45e4-812d-33b0f97325bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Question to Dense Search: What are Google's environmental initiatives?\n",
      "\n",
      "Relevance Score: 4\n",
      "\n",
      "Final Answer:\n",
      "Google's environmental initiatives include empowering individuals to take action through sustainability features in products like Google Maps, Google Nest thermostats, and Google Flights. They also work with partners and customers to collectively reduce carbon equivalent emissions. Google engages with suppliers to reduce energy consumption and greenhouse gas emissions, audits suppliers for compliance with environmental criteria, and expects suppliers to report environmental data. Google is a founding member of the iMasons Climate Accord and supports initiatives like the ReFED Catalytic Grant Fund and projects with The Nature Conservancy. They also work on risk management related to environmental impacts and sustainability. Additionally, Google invests in breakthrough innovation, supports sustainability-focused accelerators, and collaborates with partners and customers to advance technology for sustainability.\n",
      "\n",
      "\n",
      "Retrieved Documents:\n",
      "Document 1: Document ID: 12 source: dense\n",
      "Content:\n",
      "The opportunity we have through our products and \n",
      "platforms is reflected in our updated environmental sustainability strategy, which focuses on where we can make the most significant positive impact. Our work is organized around three key pillars: empowering individuals to take action, working together with our partners and customers, and operating our business sustainably.\n",
      "In 2022, we reached our goal to help 1 billion people \n",
      "make more sustainable choices through our products. We achieved this by offering sustainability features like eco-friendly routing in Google Maps, energy efficiency features in Google Nest thermostats, and carbon emissions information in Google Flights. Looking ahead, our aspiration is to help individuals, cities, and other partners collectively reduce 1 gigaton of their carbon equivalent emissions annually by 2030.\n",
      " 2\n",
      "\n",
      "Document 2: Document ID: 150 source: sparse\n",
      "Content:\n",
      "sustainability, and we’re partnering with them to develop decarbonization roadmaps and build essential data infrastructure to accurately quantify emissions and reductions across the value chain.\n",
      "We engage with our suppliers—including hardware \n",
      "manufacturing and indirect services suppliers—to help reduce their energy consumption and GHG emissions, as stated in our Supplier Code of Conduct , which all \n",
      "suppliers are required to sign. We assess suppliers’ practices to report, manage, and reduce their emissions and incorporate this into our supplier scorecard.\n",
      "Reporting  \n",
      "environmental data\n",
      "We expect all our suppliers to report environmental data,\n",
      "\n",
      "Document 3: Document ID: 311 source: dense\n",
      "Content:\n",
      "In 2022, we audited a subset of our suppliers to verify \n",
      "compliance for the following environmental criteria: implementation of environmental management systems, environmental permits and reporting, product content restrictions, and resource efficiency, as well as management of hazardous substances, wastewater,  solid waste, and air emissions.\n",
      "Googlers chat among indoor plants at our Pier 57 office in New York City.   79\n",
      "2023 Environmental Report  Public policy and advocacy\n",
      "We know that strong public policy action is critical to \n",
      "creating prosperous, equitable, and resilient low-carbon economies around the world. \n",
      "The United Nations Framework Convention on Climate \n",
      "Change (UNFCCC)’s 2015 Paris Agreement states that humanity must “keep global temperature rise this century well below 2°C above pre-industrial levels.”\n",
      " 143 Google\n",
      "\n",
      "Document 4: Document ID: 309 source: sparse\n",
      "Content:\n",
      "that enable us to ensure that those we partner with are responsible environmental stewards. Along with having suppliers evaluate their operations, we perform our own ongoing due diligence and audits to verify compliance and to understand our supply chain’s current and potential risks.\n",
      "When we find that a supplier isn’t complying, we expect\n",
      "\n",
      "Document 5: Document ID: 344 source: dense\n",
      "Content:\n",
      "iMasons Climate AccordGoogle is a founding member and part of the governing body of the iMasons Climate Accord, a coalition united on carbon reduction in digital infrastructure.\n",
      "ReFEDIn 2022, to activate industry-wide change, Google provided anchor funding to kickstart the ReFED Catalytic Grant Fund, with the goal of accelerating and scaling food waste solutions.\n",
      "The Nature Conservancy (TNC)In 2022, Google supported three of the Nature Conservancy’s watershed projects in Chile and the United States, and Google.org supported a three-phased approach to catalyze active reforestation of kelp at impactful scales. Google.org also provided a grant to TNC to develop a machine-learning-powered timber-tracing API to stop deforestation in the Amazon at scale; a team of Google engineers is working full-time for six months with TNC to develop this product as part of the Google.org Fellowship Program.\n",
      "\n",
      "Document 6: Document ID: 298 source: sparse\n",
      "Content:\n",
      "2023 Environmental Report  Risk management\n",
      "Our Enterprise Risk Management (ERM) team is responsible \n",
      "for identifying, assessing, and reporting risks related to the company’s operations, financial performance, and reputation. As with financial, operational, and strategic risks, the team assesses environmental risks as part of the company’s overall risk management framework. The risks and opportunities identified through this process support public disclosures and inform Google’s environmental sustainability strategy. Our Chief Sustainability Officer and sustainability teams work to address risks by identifying opportunities to reduce the company’s environmental impacts from its operations and value chain, and through improving climate resilience. \n",
      "Climate-related \n",
      "risks\n",
      "Climate-related risks and opportunities have long time\n",
      "\n",
      "Document 7: Document ID: 13 source: dense\n",
      "Content:\n",
      "2\n",
      "After two years of condensed reporting, we’re sharing a deeper dive into our approach in one place in our 2023 Environmental Report. In 2022, we continued to make measurable progress in many key ways, such as:\n",
      "• We enhanced and launched new sustainabilityproduct features , such as eco-friendly routing in\n",
      "Maps, which is estimated to have helped preventmore than 1.2 million metric tons of carbon emissionsfrom launch through 2022—equivalent to takingapproximately 250,000 fuel-based cars off the roadfor a year.\n",
      " 3\n",
      "• We expanded the availability of Google EarthEngine —which provides access to reliable, up-to-\n",
      "date insights on how our planet is changing—toinclude businesses and governments worldwide as anenterprise-grade service through Google Cloud.• We opened our new Bay View campus , which is\n",
      "all-electric, net water-positive, restores over 17 acresof high-value nature, and incorporates the leadingprinciples of circular design.\n",
      "\n",
      "Document 8: Document ID: 115 source: dense\n",
      "Content:\n",
      "of over 140 partner organizations.\n",
      "The Google.org Impact Challenge on Climate Innovation supports breakthrough projects that use data and technology to \n",
      "accelerate climate action.\n",
      "The journey ahead\n",
      "From measuring and monitoring changes on the Earth’s surface, improving forecast and prediction models for flooding and wildfires, optimizing operations, combining disparate data sources, and designing more efficient products, we continue to leverage our expertise in technology and apply the latest advancements to help solve global challenges.\n",
      "We believe that by working together with our partners and \n",
      "customers, we can make a real difference in addressing the challenges of climate change and ecosystem degradation. LEARN MORE\n",
      "• Data Commons\n",
      "• Environmental Insights Explorer\n",
      "• Google Cloud sustainability\n",
      "• Google Earth Engine\n",
      "• Sustainability-focused accelerators   31\n",
      "2023 Environmental Report  Operating \n",
      "sustainably\n",
      "We’re showing the way forward \n",
      "through our own operationsOur ambition\n",
      "\n",
      "Document 9: Document ID: 328 source: sparse\n",
      "Content:\n",
      "Sustainable \n",
      "consumption of \n",
      "public goods (e.g., \n",
      "“right to repair”)Google submitted comments to the European Commission’s public consultation regarding \n",
      "the promotion of repair and reuse of goods. We shared our views on the core principles to \n",
      "consider when introducing policy measures to promote repair and reuse horizontally, and for \n",
      "smartphones and tablets specifically.\n",
      "Body of European \n",
      "Regulators \n",
      "for Electronic \n",
      "Communications \n",
      "(BEREC)Google responded to a questionnaire  by BEREC in view of the development of key performance \n",
      "indicators to characterize the environmental impact of electronic communications, networks, \n",
      "devices, and services. We provided information about our environmental reporting practices \n",
      "and suggestions to help identify which indicators would provide relevant environmental \n",
      "information.\n",
      "Engagement with coalitions and sustainability initiatives\n",
      "RE-Source PlatformGoogle is a strategic partner and steering committee member of the RE-Source Platform, the\n",
      "\n",
      "Document 10: Document ID: 67 source: dense\n",
      "Content:\n",
      "Our approach\n",
      "Supporting partners\n",
      "Investing in breakthrough \n",
      "innovation\n",
      "Creating ecosystems for \n",
      "collaboration\n",
      "The journey ahead\n",
      "   21\n",
      "2023 Environmental Report  Our ambition\n",
      "We believe that Google has a unique \n",
      "opportunity that extends beyond reducing \n",
      "the environmental impacts of our own \n",
      "operations and value chain. By organizing \n",
      "information about our planet and making \n",
      "it actionable through technology and \n",
      "platforms, we can help partners and \n",
      "customers create even more positive impact.\n",
      "Digital technologies  play a critical role in industry \n",
      "transitions, allowing us to measure and track sustainability \n",
      "progress, optimize the use of resources, reduce \n",
      "greenhouse gas emissions, and enable a more circular \n",
      "economy. 50 Cloud computing and digital technologies \n",
      "underpin the transformation in many sectors, such as \n",
      "energy, transportation, and agriculture. Research  that \n",
      "we commissioned in 2022 found that 20%–25% of what’s \n",
      "required for the EU’s 2050 net-zero goal requires some\n",
      "\n",
      "Document 11: Document ID: 415 source: sparse\n",
      "Content:\n",
      "chemistry\n",
      "• Governance and engagement - Risk management; Stakeholder engagement - Supplier \n",
      "engagement\n",
      "Engagement with external targets and initiatives related to sustainable \n",
      "supply chains • Wor king together - Our approach - Supporting partners - Cloud customers and  \n",
      "commercial partners\n",
      "• Governance and engagement - PartnershipsC12. Engagement\n",
      "Goals and targets Supplier environmental assessment-related targets• Introd uction - Targets and progress summary\n",
      "• Oper ating sustainably - Circular economy - Our approach - Working with suppliers\n",
      "Performance indicators New suppliers that were screened using environmental criteria • Governance and engagement - Risk management C12. Engagement\n",
      "Supplier renewable energy• Opera ting sustainably - Net-zero carbon - Our approach - Advancing carbon-free energy - \n",
      "CFE inv estmentsC2. Risks and opportunities\n",
      "Negative environmental impacts in the supply chain and actions taken• Oper ating sustainably - Circular economy - Supply chain\n",
      "\n",
      "Document 12: Document ID: 346 source: dense\n",
      "Content:\n",
      "World Business Council for Sustainable Development (WBCSD)Google has been a member of the WBCSD for several years and participates in a number of its initiatives. \n",
      "We’re a\n",
      "ctively involved in initiatives related to improving well-being for people and the planet, including  \n",
      "shifting diets, consumer behavior changes, and regenerative agriculture. \n",
      "World Resources Institute (WRI)Google has a 13-year long relationship with WRI for impact-focused collaboration. Some key projects include developing a near-real-time land cover dataset ( Dynamic World ), deforestation monitoring and alerts ( Global \n",
      "Forest Watch ), ending commodity-driven deforestation and accelerating restoration ( Forest Data Partnership ), \n",
      "measuring and mitigating extreme heat ( supported by Google.org ), and educating stakeholders on 24/7 CFE.   84\n",
      "2023 Environmental Report  Awards and recognition\n",
      "2022 CDP Climate Change A List  \n",
      "Alphabet has been named to CDP’s Climate Change A list,\n",
      "\n",
      "Document 13: Document ID: 139 source: sparse\n",
      "Content:\n",
      "development and deployment of these materials.\n",
      "In 2022, we filed a patent for using machine \n",
      "learning technology to improve our ability to prevent emissions from refrigerant leaks.\n",
      "Data centers\n",
      "Google’s data centers are the engine of our company, powering products like Gmail, Google Cloud, Search, and YouTube for billions of people around the world. We’ve worked to make Google’s data centers some of the most efficient in the world, improving their environmental performance even as demand for our products has risen. We’ve done this by designing, building, and operating each one to maximize efficient use of energy, water, \n",
      "and ma\n",
      "terials.\n",
      "Our long-standing data center efficiency  efforts are\n",
      "\n",
      "Document 14: Document ID: 111 source: dense\n",
      "Content:\n",
      "Googlers collaborate in the Event Center at our Bay View campus.   30\n",
      "2023 Environmental Report  Google for Startups\n",
      "By investing early in technologies aimed at tackling \n",
      "sustainability challenges like climate change, we have the potential to move the needle on sustainability and positively impact our planet. We have a portfolio of sustainability-focused accelerators , which support  \n",
      "early stage innovations to grow and scale.\n",
      "Google for Startups \n",
      "Accelerator\n",
      "Google for Startups is working to identify, support, and\n",
      "\n",
      "Document 15: Document ID: 432 source: sparse\n",
      "Content:\n",
      "2023 Environmental Report  market structures. If no such structure exists, then Google defines the grid \n",
      "region as the electricity-balancing authority where our data centers are \n",
      "located. Outside of the United States, the grid region most often refers to \n",
      "the geographic boundary of a country, because most grid system operators \n",
      "operate at the national level. Certain regions that span multiple countries \n",
      "are well interconnected and could be considered as one grid; however, \n",
      "our grid mix calculations already include import and export considerations \n",
      "and therefore take into account power flows from neighboring grids. In \n",
      "the future, we may update our definition as we work with grid operators to \n",
      "better understand how transmission constraints or congestion impact CFE \n",
      "measurement within and across grid regions.\n",
      "91 Contracted CFE is the hourly electricity production from clean energy \n",
      "projects whose electricity and associated environmental attributes are\n",
      "\n",
      "Document 16: Document ID: 66 source: dense\n",
      "Content:\n",
      "the United States for pre-owned products, such as used and refurbished products. The journey \n",
      "ahead\n",
      "While a single individual’s actions may seem small, when \n",
      "billions of people have the tools to make more sustainable decisions, they add up to have a meaningful impact on their communities and the entire planet. \n",
      "We’re excited by the opportunity to enable climate and \n",
      "environmental action far beyond Google’s direct impact, through information and innovation.\n",
      "LEARN MORE\n",
      " • Empowering with technology\n",
      " • Google Maps eco-friendly routing\n",
      " • Searching for sustainability with Google\n",
      " • Supporting a clean energy future with Nest Renew\n",
      " • The search for sustainability   20\n",
      "2023 Environmental Report  Working \n",
      "together\n",
      "We’re working together with our \n",
      "partners and customers to advance technology for sustainabilityOur ambition\n",
      "Our approach\n",
      "Supporting partners\n",
      "Investing in breakthrough \n",
      "innovation\n",
      "Creating ecosystems for \n",
      "collaboration\n",
      "The journey ahead\n",
      "   21\n",
      "2023 Environmental Report  Our ambition\n",
      "\n",
      "Document 17: Document ID: 91 source: sparse\n",
      "Content:\n",
      "EV Suitability Assessment helps organizations monitor their fleet of vehicles and make choices that minimize environmental impact.\n",
      "Data analytics tools from Google Cloud are also helping \n",
      "airlines. Lufthansa Group partnered with Google Cloud \n",
      "and Google Research to develop a platform that facilitates better planning and management of daily flight operations.\n",
      "We’re helping organizations harness \n",
      "the power of data and AI to drive more intelligent supply chains.Renewable energy\n",
      "Wind farms are an important source of carbon-free electricity, but wind can fluctuate depending on the weather. Through Google Cloud, customers like Engie  (a global energy and renewables supplier) can optimize their wind portfolio in short-term power markets by predicting wind power output 36 hours ahead of actual generation  and making optimal hourly delivery \n",
      "commitments to the grid, a full day in advance.  \n",
      "Sustainability partner \n",
      "solutions\n",
      "Partner solutions are important to scale the impact for our\n",
      "\n",
      "Document 18: Document ID: 262 source: dense\n",
      "Content:\n",
      "135 \n",
      "Preserving nature is critical both to mitigating climate  \n",
      "change and adapting to it. We want nature and people to flourish together in the communities that Google calls  home, as well as the ecosystems where we source food  for the hundreds of cafes we operate. Our approach\n",
      "We strive to protect and enhance nature and biodiversity through our campuses and technology.\n",
      "Google has offices in nearly 60 countries around the world \n",
      "(as of year-end 2022). In these locations, we aim to protect and enhance nature and biodiversity through a four-pillar approach that starts with building biodiversity at our own office and campus developments, as well as protecting nature and making it more accessible in the surrounding communities where we operate (see Figure 24). \n",
      "Our approach further focuses on sourcing responsibly\n",
      "\n",
      "Document 19: Document ID: 22 source: sparse\n",
      "Content:\n",
      "cled/ Ongoing  36% 41% 2025our consumer hardware product portfolio by 2025 renewable material (see pg. 62 )\n",
      "% plastic-free Ongoing  Make product packaging 100% plastic-free by 2025 97% 96% 2025packaging (see pg. 63 )\n",
      "Significant  Achieve UL 2799 Zero Waste to Landfill certification at all final \n",
      "Supply chain % of sites certified 9% 90% 2022 progress  assembly consumer hardware manufacturing sites by 2022\n",
      "(see pg. 65 )   9\n",
      "2023 Environmental Report  \n",
      "Emerging opportunities\n",
      "As the world becomes increasingly aware of the need for sustainability, individuals, businesses, and communities are  \n",
      "looking for new ways to reduce their environmental impact. Artificial intelligence (AI) and the power of information to help individuals and organizations reduce emissions are two emerging opportunities that Google is focusing on to help build a more sustainable future.\n",
      "AI for sustainability\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Question - Submitted to the hybrid / multi-vector search\n",
    "result = rag_chain_hybrid.invoke(user_query)\n",
    "retrieved_docs = result['context']\n",
    "\n",
    "print(f\"Original Question to Dense Search: {user_query}\\n\")\n",
    "print(f\"Relevance Score: {result['answer']['relevance_score']}\\n\")\n",
    "print(f\"Final Answer:\\n{result['answer']['final_answer']}\\n\\n\")\n",
    "print(\"Retrieved Documents:\")\n",
    "for i, doc in enumerate(retrieved_docs, start=1):\n",
    "    print(f\"Document {i}: Document ID: {doc.metadata['id']} source: {doc.metadata['source']}\")\n",
    "    print(f\"Content:\\n{doc.page_content}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vt0WA5gbZrZm",
   "metadata": {
    "id": "vt0WA5gbZrZm"
   },
   "source": [
    "#### SIMILARITY SEARCH ONLY\n",
    "Google's environmental initiatives include empowering individuals to take action, working together with partners and customers, operating sustainably, achieving net-zero carbon emissions, water stewardship, and promoting a circular economy. They have implemented sustainability features in products like Google Maps, Google Nest thermostats, and Google Flights to help individuals make more sustainable choices. Google also supports various environmental organizations and initiatives, such as the iMasons Climate Accord, ReFED, and The Nature Conservancy, to accelerate climate action and address environmental challenges. Additionally, Google is involved in public policy advocacy and is committed to reducing its environmental impact through its operations and value chain.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p0vqTbfKZcNo",
   "metadata": {
    "id": "p0vqTbfKZcNo"
   },
   "source": [
    "#### HYBRID SEARCH\n",
    "\n",
    "Google's environmental initiatives include empowering individuals to take action, working together with partners and customers, operating sustainably, achieving net-zero carbon emissions, focusing on water stewardship, promoting a circular economy, engaging with suppliers to reduce energy consumption and greenhouse gas emissions, and reporting environmental data. They also support public policy and advocacy for low-carbon economies, participate in initiatives like the iMasons Climate Accord and ReFED, and support projects with organizations like The Nature Conservancy. Additionally, Google is involved in initiatives with the World Business Council for Sustainable Development and the World Resources Institute to improve well-being for people and the planet. They are also working on using technology and platforms to organize information about the planet and make it actionable to help partners and customers create a positive impact."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "X7Jsr-wYaMtQ",
   "metadata": {
    "id": "X7Jsr-wYaMtQ"
   },
   "source": [
    "### SYNTHETIC DATA GENERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aDqo-x7g3gS5",
   "metadata": {
    "executionInfo": {
     "elapsed": 1264,
     "status": "ok",
     "timestamp": 1718829861389,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "aDqo-x7g3gS5"
   },
   "outputs": [],
   "source": [
    "# generator with openai models\n",
    "generator = TestsetGenerator.from_langchain(\n",
    "    generator_llm,\n",
    "    critic_llm,\n",
    "    embedding_function\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "Sa8ZqrCnaP6w",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84,
     "referenced_widgets": [
      "b62df6984dae4804961b831230d27726",
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      "aaf4bf8c0e044d3fb4ff3c38e9ecfc0d",
      "2958881693c94a27b7927af3bcc144f5",
      "894b24a4c6b14591a12772cb96b63faa",
      "8c38f5d864114f178e0875c8795e91d8",
      "a75976d6f7fb4fa9b98b218d02a738da",
      "d733475b072d4c7894ad6ecd2ebc6029",
      "e2444fdd626045a9a323a792512a1bf4",
      "7927111dda3a41808138e3db8b482e01",
      "4f7773428f2c49cba7a44b871c4433ec",
      "e953963c139c4cebb1b81e18a5d51d71",
      "56108ddc649541afa9317606df743043",
      "d7f68735fb2543f5ba8481f5dc8e48b0",
      "f9bb615eb360473d9859ca45ebc50f9f",
      "d7f39e0e03c64520916b54105d3015cb",
      "bac8c40fa6b8497a9d0bf265e4f07be9",
      "ff412fba3e4d4c69b2b8199440de0ebc",
      "be78e520f0e44c55963d01864c0b060c",
      "55447c0281894d14a62988c35035be8c",
      "48bf2662ca5c44ab9d5701cf3d50b9f7",
      "2516efe6c2ba47ef8abfb5a85a802f29"
     ]
    },
    "executionInfo": {
     "elapsed": 69414,
     "status": "ok",
     "timestamp": 1718829937522,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "Sa8ZqrCnaP6w",
    "outputId": "2755b2a8-bea3-4e41-86f8-d56d8fbb4acd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Filename and doc_id are the same for all nodes.                   \n",
      "Generating: 100%|██████████| 10/10 [00:54<00:00,  5.47s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testset DataFrame saved successfully in the local directory.\n"
     ]
    }
   ],
   "source": [
    "# Create a list of Document objects from the chunks\n",
    "documents = [Document(page_content=chunk) for chunk in splits]\n",
    "\n",
    "#### FOR FOLLOWING CODE: Uncomment and run once to generate source for test dataset! ####\n",
    "# generate testset -\n",
    "testset = generator.generate_with_langchain_docs(\n",
    "    documents,\n",
    "    test_size=10,\n",
    "    distributions={\n",
    "        simple: 0.5,\n",
    "        reasoning: 0.25,\n",
    "        multi_context: 0.25\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "GGHFs-I8C8TB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 160,
     "status": "ok",
     "timestamp": 1718829952300,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "GGHFs-I8C8TB",
    "outputId": "4fef0c58-5d5b-4997-9359-f783f3a1fde1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testset DataFrame saved successfully in the local directory.\n"
     ]
    }
   ],
   "source": [
    "# comparison dataframe\n",
    "testset_df = testset.to_pandas()\n",
    "\n",
    "# save dataframes to CSV files in the specified directory\n",
    "testset_df.to_csv(os.path.join('testset_data.csv'), index=False)\n",
    "\n",
    "print(\"testset DataFrame saved successfully in the local directory.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "GlvSvmK_csVe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "executionInfo": {
     "elapsed": 150,
     "status": "ok",
     "timestamp": 1718829954195,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "GlvSvmK_csVe",
    "outputId": "ce04e8bb-ba29-4b32-9b3b-19ee08b0213a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testset DataFrame loaded successfully from local directory.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>contexts</th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>evolution_type</th>\n",
       "      <th>metadata</th>\n",
       "      <th>episode_done</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What are Scope 1 GHG emissions and what source...</td>\n",
       "      <td>['We source the global warming potentials (GWP...</td>\n",
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       "      <td>How does increasing protection status contribu...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>How much water have Google's contracted waters...</td>\n",
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       "      <td>271 million gallons of water</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>['We’ve consistently supported strong climate ...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How is Google supporting The Nature Conservanc...</td>\n",
       "      <td>['iMasons Climate AccordGoogle is a founding m...</td>\n",
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       "    </tr>\n",
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       "                                            question  \\\n",
       "0  What are Scope 1 GHG emissions and what source...   \n",
       "1  How does increasing protection status contribu...   \n",
       "2  How much water have Google's contracted waters...   \n",
       "3  How has Google demonstrated its support for st...   \n",
       "4  How is Google supporting The Nature Conservanc...   \n",
       "\n",
       "                                            contexts  \\\n",
       "0  ['We source the global warming potentials (GWP...   \n",
       "1  ['actions—like habitat restoration, increasing...   \n",
       "2  ['In addition to focusing on responsible water...   \n",
       "3  ['We’ve consistently supported strong climate ...   \n",
       "4  ['iMasons Climate AccordGoogle is a founding m...   \n",
       "\n",
       "                                        ground_truth evolution_type metadata  \\\n",
       "0  Scope 1 GHG emissions are direct emissions fro...         simple     [{}]   \n",
       "1  Increasing protection status contributes to th...         simple     [{}]   \n",
       "2                       271 million gallons of water         simple     [{}]   \n",
       "3  Google has consistently supported strong clima...         simple     [{}]   \n",
       "4  Google supported The Nature Conservancy in dev...         simple     [{}]   \n",
       "\n",
       "   episode_done  \n",
       "0          True  \n",
       "1          True  \n",
       "2          True  \n",
       "3          True  \n",
       "4          True  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pull data from saved testset, rather than generating above\n",
    "### load dataframs from CSV file\n",
    "saved_testset_df = pd.read_csv(os.path.join('testset_data.csv'))\n",
    "print(\"testset DataFrame loaded successfully from local directory.\")\n",
    "saved_testset_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "DJM33u_1c4-D",
   "metadata": {
    "id": "DJM33u_1c4-D"
   },
   "source": [
    "### PREPARE SIMILARITY SEARCH DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7RcpHhh2c4QH",
   "metadata": {
    "executionInfo": {
     "elapsed": 160,
     "status": "ok",
     "timestamp": 1718830146610,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "7RcpHhh2c4QH"
   },
   "outputs": [],
   "source": [
    "# Convert the DataFrame to a dictionary\n",
    "saved_testing_data = saved_testset_df.astype(str).to_dict(orient='list')\n",
    "\n",
    "# Create the testing_dataset\n",
    "saved_testing_dataset = Dataset.from_dict(saved_testing_data)\n",
    "\n",
    "# Update the testing_dataset to include only these columns -\n",
    "# \"question\", \"ground_truth\", \"answer\", \"contexts\"\n",
    "saved_testing_dataset_sm = saved_testing_dataset.remove_columns([\"evolution_type\", \"episode_done\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "pVBD6b1Zc7n6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 167,
     "status": "ok",
     "timestamp": 1718830148680,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "pVBD6b1Zc7n6",
    "outputId": "24fd74d3-f2c4-4b46-f04d-cccb04dea5d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['question', 'contexts', 'ground_truth', 'metadata'],\n",
       "    num_rows: 10\n",
       "})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saved_testing_dataset_sm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3TNKvrXOerLq",
   "metadata": {
    "id": "3TNKvrXOerLq"
   },
   "source": [
    "### EVAL SETS FOR EACH CHAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6HrK-klXqn4v",
   "metadata": {
    "executionInfo": {
     "elapsed": 241,
     "status": "ok",
     "timestamp": 1718830746371,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "6HrK-klXqn4v"
   },
   "outputs": [],
   "source": [
    "# Function to generate answers using the RAG chain\n",
    "def generate_answer(question, ground_truth, rag_chain):\n",
    "    result = rag_chain.invoke(question)\n",
    "    return {\n",
    "        \"question\": question,\n",
    "        \"answer\": result[\"answer\"][\"final_answer\"],\n",
    "        \"contexts\": [doc.page_content for doc in result[\"context\"]],\n",
    "        \"ground_truth\": ground_truth\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "iXgC8QvRqqop",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
      "58137e596a77408d9f246e16a96257c5",
      "2fa1eb0f660e412c8b7ce77fd1b308fb",
      "37d67a1a2b4c40a5abf6b9060a423802",
      "42661ceb3a1249ba90127e507df434b7",
      "c75584e5bedb48788362b0b051fb65c5",
      "4868ffb0f6bf4ab5a293486efe2c1ea4",
      "60ee238c73c94012bec6496a3c8a8181",
      "daca9ef8d18d467c9385372db3976fc5",
      "39a876216d6c40839c65750c95b9cee4",
      "e515bcc89c7042318aa3880f2988fc66",
      "ba35443cb4c646a59244bd173dec2003"
     ]
    },
    "executionInfo": {
     "elapsed": 23947,
     "status": "ok",
     "timestamp": 1718830771886,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "iXgC8QvRqqop",
    "outputId": "13d416fa-0365-4129-d041-6492efb1450a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter 'function'=<function <lambda> at 0xffff2590f060> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n",
      "Map: 100%|██████████| 10/10 [00:16<00:00,  1.67s/ examples]\n"
     ]
    }
   ],
   "source": [
    "# Add the \"question\", \"answer\", \"contexts\", and \"ground_truth\" to the testing_dataset\n",
    "testing_dataset_similarity = saved_testing_dataset_sm.map(lambda x: generate_answer(x[\"question\"], x[\"ground_truth\"], rag_chain_similarity), remove_columns=saved_testing_dataset_sm.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ttDBKAhhshdY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "3bf075040114456a805e1e29548cabd3",
      "45407e215ea94124bb98dd04e1dfd53f",
      "eb82156cdf564ef1a5e94ff3fba5fd38",
      "bd056017567d4299bae2b2cfd51ef933",
      "9c86ff1aab9f4ee2b20240967a61390f",
      "674707c4b60a40289da685c71a09901b",
      "2952c7b476094838aaa6876fdfb71d58",
      "12f4e8b2b5e540f0b9a9f04fa87128da",
      "3fa021834120431d9aff9d0e4db0520f",
      "dcd2fde7910841ed86bd8a808fdffd0f",
      "3a67dd29b0834433ba8d09a74e8260dd"
     ]
    },
    "executionInfo": {
     "elapsed": 21566,
     "status": "ok",
     "timestamp": 1718830793441,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "ttDBKAhhshdY",
    "outputId": "6ea9af2d-1d32-49e8-bf28-0b030c29ce8e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 10/10 [00:18<00:00,  1.83s/ examples]\n"
     ]
    }
   ],
   "source": [
    "# Add the \"question\", \"answer\", \"contexts\", and \"ground_truth\" to the testing_dataset\n",
    "testing_dataset_hybrid = saved_testing_dataset_sm.map(lambda x: generate_answer(x[\"question\"], x[\"ground_truth\"], rag_chain_hybrid), remove_columns=saved_testing_dataset_sm.column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oGmEJp6ZddS2",
   "metadata": {
    "id": "oGmEJp6ZddS2"
   },
   "source": [
    "### EVAL SCORING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "DQV_SbQcc7ga",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "e59eea658b0b4cbf9c72de3506ad654f",
      "aa927d6b5a0f4d05948b1e6e466a788a",
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      "10d97d3434b247ec90c91183e9011695",
      "65a42bc7aa0c400b8e13e4b19f1ce2fc",
      "7ad7b6bcf54e41b9b963365ff2f0f874",
      "0a3544e31ec34b108086b75011a4b20f",
      "7c771bc4ae164b41bb82081d1dada922",
      "471c110824914083a84c06b1a30276b9",
      "5ea64bd753494d36a6203c106b6c9a8f",
      "d58b5e71c71a435f8c162fd2415327c2"
     ]
    },
    "executionInfo": {
     "elapsed": 43714,
     "status": "ok",
     "timestamp": 1718830999864,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "DQV_SbQcc7ga",
    "outputId": "17583b22-d191-4a33-db7c-bbf430d54c35"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 60/60 [00:48<00:00,  1.24it/s]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>contexts</th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>answer</th>\n",
       "      <th>faithfulness</th>\n",
       "      <th>answer_relevancy</th>\n",
       "      <th>context_precision</th>\n",
       "      <th>context_recall</th>\n",
       "      <th>answer_correctness</th>\n",
       "      <th>answer_similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What are Scope 1 GHG emissions and what source...</td>\n",
       "      <td>[We source the global warming potentials (GWP)...</td>\n",
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       "      <td>How does increasing protection status contribu...</td>\n",
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       "      <td>0.620180</td>\n",
       "      <td>0.980719</td>\n",
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       "      <td>How much water have Google's contracted waters...</td>\n",
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       "      <td>As of the end of 2022, Google's contracted wat...</td>\n",
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       "      <td>How has Google demonstrated its support for st...</td>\n",
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       "      <td>0.862131</td>\n",
       "      <td>0.948448</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How is Google supporting The Nature Conservanc...</td>\n",
       "      <td>[iMasons Climate AccordGoogle is a founding me...</td>\n",
       "      <td>Google supported The Nature Conservancy in dev...</td>\n",
       "      <td>Google is supporting The Nature Conservancy in...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.934022</td>\n",
       "      <td>0.884354</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.748500</td>\n",
       "      <td>0.993999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>How is Google helping public health in Chile t...</td>\n",
       "      <td>[public health in the communities that make up...</td>\n",
       "      <td>Google is helping public health in Chile throu...</td>\n",
       "      <td>Google is helping public health in Chile throu...</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.943325</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.715686</td>\n",
       "      <td>0.987745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>How is Google working with UN-Energy and other...</td>\n",
       "      <td>[C40 CitiesC40 and Google launched  the 24/7 C...</td>\n",
       "      <td>Google is working with UN-Energy and others to...</td>\n",
       "      <td>Google is working with UN-Energy and others to...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.915698</td>\n",
       "      <td>0.920685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.657430</td>\n",
       "      <td>0.963053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>How does Green Light AI help city traffic engi...</td>\n",
       "      <td>[Jeff DeanChief ScientistGoogle DeepMind and \\...</td>\n",
       "      <td>Green Light AI helps city traffic engineers op...</td>\n",
       "      <td>Green Light AI helps city traffic engineers op...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.979720</td>\n",
       "      <td>0.775397</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.849183</td>\n",
       "      <td>0.996733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>How does Google support sustainable city proje...</td>\n",
       "      <td>[Global Covenant of Mayors for Climate &amp; Energ...</td>\n",
       "      <td>Google supports sustainable city projects with...</td>\n",
       "      <td>Google supports sustainable city projects with...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906041</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.746111</td>\n",
       "      <td>0.984446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>How does Google aim to replenish more freshwat...</td>\n",
       "      <td>[In addition to focusing on responsible water ...</td>\n",
       "      <td>Google aims to replenish 20% more freshwater t...</td>\n",
       "      <td>Google aims to replenish more freshwater than ...</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.963201</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.843718</td>\n",
       "      <td>0.974871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question  \\\n",
       "0  What are Scope 1 GHG emissions and what source...   \n",
       "1  How does increasing protection status contribu...   \n",
       "2  How much water have Google's contracted waters...   \n",
       "3  How has Google demonstrated its support for st...   \n",
       "4  How is Google supporting The Nature Conservanc...   \n",
       "5  How is Google helping public health in Chile t...   \n",
       "6  How is Google working with UN-Energy and other...   \n",
       "7  How does Green Light AI help city traffic engi...   \n",
       "8  How does Google support sustainable city proje...   \n",
       "9  How does Google aim to replenish more freshwat...   \n",
       "\n",
       "                                            contexts  \\\n",
       "0  [We source the global warming potentials (GWP)...   \n",
       "1  [actions—like habitat restoration, increasing ...   \n",
       "2  [In addition to focusing on responsible water ...   \n",
       "3  [We’ve consistently supported strong climate p...   \n",
       "4  [iMasons Climate AccordGoogle is a founding me...   \n",
       "5  [public health in the communities that make up...   \n",
       "6  [C40 CitiesC40 and Google launched  the 24/7 C...   \n",
       "7  [Jeff DeanChief ScientistGoogle DeepMind and \\...   \n",
       "8  [Global Covenant of Mayors for Climate & Energ...   \n",
       "9  [In addition to focusing on responsible water ...   \n",
       "\n",
       "                                        ground_truth  \\\n",
       "0  Scope 1 GHG emissions are direct emissions fro...   \n",
       "1  Increasing protection status contributes to th...   \n",
       "2                       271 million gallons of water   \n",
       "3  Google has consistently supported strong clima...   \n",
       "4  Google supported The Nature Conservancy in dev...   \n",
       "5  Google is helping public health in Chile throu...   \n",
       "6  Google is working with UN-Energy and others to...   \n",
       "7  Green Light AI helps city traffic engineers op...   \n",
       "8  Google supports sustainable city projects with...   \n",
       "9  Google aims to replenish 20% more freshwater t...   \n",
       "\n",
       "                                              answer  faithfulness  \\\n",
       "0  Scope 1 GHG emissions are direct emissions fro...         1.000   \n",
       "1  Increasing protection status contributes to th...         1.000   \n",
       "2  As of the end of 2022, Google's contracted wat...         1.000   \n",
       "3  Google has demonstrated its support for strong...         1.000   \n",
       "4  Google is supporting The Nature Conservancy in...         1.000   \n",
       "5  Google is helping public health in Chile throu...         0.875   \n",
       "6  Google is working with UN-Energy and others to...         1.000   \n",
       "7  Green Light AI helps city traffic engineers op...         1.000   \n",
       "8  Google supports sustainable city projects with...         1.000   \n",
       "9  Google aims to replenish more freshwater than ...         0.900   \n",
       "\n",
       "   answer_relevancy  context_precision  context_recall  answer_correctness  \\\n",
       "0          0.966142           0.959375             1.0            1.000000   \n",
       "1          1.000000           0.895139             0.5            0.620180   \n",
       "2          1.000000           0.870139             1.0            0.717243   \n",
       "3          0.980116           1.000000             1.0            0.862131   \n",
       "4          0.934022           0.884354             1.0            0.748500   \n",
       "5          0.943325           0.850000             1.0            0.715686   \n",
       "6          0.915698           0.920685             1.0            0.657430   \n",
       "7          0.979720           0.775397             1.0            0.849183   \n",
       "8          1.000000           0.906041             1.0            0.746111   \n",
       "9          0.963201           1.000000             1.0            0.843718   \n",
       "\n",
       "   answer_similarity  \n",
       "0           1.000000  \n",
       "1           0.980719  \n",
       "2           0.868974  \n",
       "3           0.948448  \n",
       "4           0.993999  \n",
       "5           0.987745  \n",
       "6           0.963053  \n",
       "7           0.996733  \n",
       "8           0.984446  \n",
       "9           0.974871  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similarity search score\n",
    "score_similarity = evaluate(\n",
    "    testing_dataset_similarity,\n",
    "    metrics=[\n",
    "        faithfulness,\n",
    "        answer_relevancy,\n",
    "        context_precision,\n",
    "        context_recall,\n",
    "        answer_correctness,\n",
    "        answer_similarity\n",
    "    ]\n",
    ")\n",
    "similarity_df = score_similarity.to_pandas()\n",
    "similarity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4V23UEX6c7bR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "43c7f99fa74e4357b1daee0d43b490c0",
      "ce0521c6353c4cc9b9c9f716cd4678c2",
      "4d5b08d1116e454cbc84ce78de9cd8b4",
      "0ae12e9306214e47803d9691fcc5b4d6",
      "e81fe3e1c7b7481fbaff4d5ea49d95fa",
      "3deab8857924429c97cba7ce298dd894",
      "2b1c5df7efc342188115c7e4eb80b8f3",
      "53d769cfe9044b1b9c24a6a60a2fcf9b",
      "cd8c076c31af4dab9b9102e7931d8e72",
      "1ee67a4012794b4ea564d2a6a0520695",
      "04f32866c81e481db0ab94611ee2dd49"
     ]
    },
    "executionInfo": {
     "elapsed": 147338,
     "status": "ok",
     "timestamp": 1718831155665,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "4V23UEX6c7bR",
    "outputId": "0370dfdf-1df5-40ba-a7d5-56a1bc20fb02"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 60/60 [02:26<00:00,  2.44s/it]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>contexts</th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>answer</th>\n",
       "      <th>faithfulness</th>\n",
       "      <th>answer_relevancy</th>\n",
       "      <th>context_precision</th>\n",
       "      <th>context_recall</th>\n",
       "      <th>answer_correctness</th>\n",
       "      <th>answer_similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What are Scope 1 GHG emissions and what source...</td>\n",
       "      <td>[We source the global warming potentials (GWP)...</td>\n",
       "      <td>Scope 1 GHG emissions are direct emissions fro...</td>\n",
       "      <td>Scope 1 GHG emissions are direct emissions fro...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966142</td>\n",
       "      <td>0.954334</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.784497</td>\n",
       "      <td>0.995131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>How does increasing protection status contribu...</td>\n",
       "      <td>[actions—like habitat restoration, increasing ...</td>\n",
       "      <td>Increasing protection status contributes to th...</td>\n",
       "      <td>Increasing protection status, such as through ...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.973556</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.365369</td>\n",
       "      <td>0.961474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>How much water have Google's contracted waters...</td>\n",
       "      <td>[In addition to focusing on responsible water ...</td>\n",
       "      <td>271 million gallons of water</td>\n",
       "      <td>As of the end of 2022, Google's contracted wat...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987224</td>\n",
       "      <td>0.784753</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.717243</td>\n",
       "      <td>0.868974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>How has Google demonstrated its support for st...</td>\n",
       "      <td>[We’ve consistently supported strong climate p...</td>\n",
       "      <td>Google has consistently supported strong clima...</td>\n",
       "      <td>Google has demonstrated its support for strong...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.912946</td>\n",
       "      <td>0.962130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How is Google supporting The Nature Conservanc...</td>\n",
       "      <td>[iMasons Climate AccordGoogle is a founding me...</td>\n",
       "      <td>Google supported The Nature Conservancy in dev...</td>\n",
       "      <td>Google is supporting The Nature Conservancy by...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962428</td>\n",
       "      <td>0.786993</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.747973</td>\n",
       "      <td>0.991890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>How is Google helping public health in Chile t...</td>\n",
       "      <td>[and UN-Energy  to help grow the movement to e...</td>\n",
       "      <td>Google is helping public health in Chile throu...</td>\n",
       "      <td>Google is helping public health in Chile throu...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.936593</td>\n",
       "      <td>0.756368</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.747436</td>\n",
       "      <td>0.989744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>How is Google working with UN-Energy and other...</td>\n",
       "      <td>[and UN-Energy  to help grow the movement to e...</td>\n",
       "      <td>Google is working with UN-Energy and others to...</td>\n",
       "      <td>Google is working with UN-Energy and others to...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955058</td>\n",
       "      <td>0.801851</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.511077</td>\n",
       "      <td>0.953400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>How does Green Light AI help city traffic engi...</td>\n",
       "      <td>[Jeff DeanChief ScientistGoogle DeepMind and \\...</td>\n",
       "      <td>Green Light AI helps city traffic engineers op...</td>\n",
       "      <td>Green Light AI helps city traffic engineers op...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979720</td>\n",
       "      <td>0.591087</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.849183</td>\n",
       "      <td>0.996733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>How does Google support sustainable city proje...</td>\n",
       "      <td>[Global Covenant of Mayors for Climate &amp; Energ...</td>\n",
       "      <td>Google supports sustainable city projects with...</td>\n",
       "      <td>Google supports sustainable city projects with...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977014</td>\n",
       "      <td>0.913814</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.707946</td>\n",
       "      <td>0.985630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>How does Google aim to replenish more freshwat...</td>\n",
       "      <td>[In addition to focusing on responsible water ...</td>\n",
       "      <td>Google aims to replenish 20% more freshwater t...</td>\n",
       "      <td>Google aims to replenish more freshwater than ...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.964121</td>\n",
       "      <td>0.990136</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.829982</td>\n",
       "      <td>0.986595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question  \\\n",
       "0  What are Scope 1 GHG emissions and what source...   \n",
       "1  How does increasing protection status contribu...   \n",
       "2  How much water have Google's contracted waters...   \n",
       "3  How has Google demonstrated its support for st...   \n",
       "4  How is Google supporting The Nature Conservanc...   \n",
       "5  How is Google helping public health in Chile t...   \n",
       "6  How is Google working with UN-Energy and other...   \n",
       "7  How does Green Light AI help city traffic engi...   \n",
       "8  How does Google support sustainable city proje...   \n",
       "9  How does Google aim to replenish more freshwat...   \n",
       "\n",
       "                                            contexts  \\\n",
       "0  [We source the global warming potentials (GWP)...   \n",
       "1  [actions—like habitat restoration, increasing ...   \n",
       "2  [In addition to focusing on responsible water ...   \n",
       "3  [We’ve consistently supported strong climate p...   \n",
       "4  [iMasons Climate AccordGoogle is a founding me...   \n",
       "5  [and UN-Energy  to help grow the movement to e...   \n",
       "6  [and UN-Energy  to help grow the movement to e...   \n",
       "7  [Jeff DeanChief ScientistGoogle DeepMind and \\...   \n",
       "8  [Global Covenant of Mayors for Climate & Energ...   \n",
       "9  [In addition to focusing on responsible water ...   \n",
       "\n",
       "                                        ground_truth  \\\n",
       "0  Scope 1 GHG emissions are direct emissions fro...   \n",
       "1  Increasing protection status contributes to th...   \n",
       "2                       271 million gallons of water   \n",
       "3  Google has consistently supported strong clima...   \n",
       "4  Google supported The Nature Conservancy in dev...   \n",
       "5  Google is helping public health in Chile throu...   \n",
       "6  Google is working with UN-Energy and others to...   \n",
       "7  Green Light AI helps city traffic engineers op...   \n",
       "8  Google supports sustainable city projects with...   \n",
       "9  Google aims to replenish 20% more freshwater t...   \n",
       "\n",
       "                                              answer  faithfulness  \\\n",
       "0  Scope 1 GHG emissions are direct emissions fro...      1.000000   \n",
       "1  Increasing protection status, such as through ...      0.875000   \n",
       "2  As of the end of 2022, Google's contracted wat...      1.000000   \n",
       "3  Google has demonstrated its support for strong...      1.000000   \n",
       "4  Google is supporting The Nature Conservancy by...      1.000000   \n",
       "5  Google is helping public health in Chile throu...      0.833333   \n",
       "6  Google is working with UN-Energy and others to...      1.000000   \n",
       "7  Green Light AI helps city traffic engineers op...      1.000000   \n",
       "8  Google supports sustainable city projects with...      1.000000   \n",
       "9  Google aims to replenish more freshwater than ...      0.750000   \n",
       "\n",
       "   answer_relevancy  context_precision  context_recall  answer_correctness  \\\n",
       "0          0.966142           0.954334            1.00            0.784497   \n",
       "1          0.973556           0.833333            0.50            0.365369   \n",
       "2          0.987224           0.784753            1.00            0.717243   \n",
       "3          0.950611           1.000000            1.00            0.912946   \n",
       "4          0.962428           0.786993            1.00            0.747973   \n",
       "5          0.936593           0.756368            1.00            0.747436   \n",
       "6          0.955058           0.801851            0.75            0.511077   \n",
       "7          0.979720           0.591087            1.00            0.849183   \n",
       "8          0.977014           0.913814            1.00            0.707946   \n",
       "9          0.964121           0.990136            1.00            0.829982   \n",
       "\n",
       "   answer_similarity  \n",
       "0           0.995131  \n",
       "1           0.961474  \n",
       "2           0.868974  \n",
       "3           0.962130  \n",
       "4           0.991890  \n",
       "5           0.989744  \n",
       "6           0.953400  \n",
       "7           0.996733  \n",
       "8           0.985630  \n",
       "9           0.986595  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarity search score\n",
    "score_hybrid = evaluate(\n",
    "    testing_dataset_hybrid,\n",
    "    metrics=[\n",
    "        faithfulness,\n",
    "        answer_relevancy,\n",
    "        context_precision,\n",
    "        context_recall,\n",
    "        answer_correctness,\n",
    "        answer_similarity\n",
    "    ]\n",
    ")\n",
    "hybrid_df = score_hybrid.to_pandas()\n",
    "hybrid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZRPKy242R7UF",
   "metadata": {
    "id": "ZRPKy242R7UF"
   },
   "source": [
    "### ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "LxHAZHpzvt5H",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 150,
     "status": "ok",
     "timestamp": 1718831193985,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "LxHAZHpzvt5H",
    "outputId": "6e7dda8f-d2c9-49d9-88b9-68f6bef4b983"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframes saved successfully in the local directory.\n"
     ]
    }
   ],
   "source": [
    "# Analysis that consolidates everything into easier-to-read scores\n",
    "# key columns to compare\n",
    "key_columns = [\n",
    "    'faithfulness',\n",
    "    'answer_relevancy',\n",
    "    'context_precision',\n",
    "    'context_recall',\n",
    "    'answer_correctness',\n",
    "    'answer_similarity'\n",
    "]\n",
    "\n",
    "# mean scores for each key column in similarity_df\n",
    "similarity_means = similarity_df[key_columns].mean()\n",
    "\n",
    "# mean scores for each key column in hybrid_df\n",
    "hybrid_means = hybrid_df[key_columns].mean()\n",
    "\n",
    "# comparison dataframe\n",
    "comparison_df = pd.DataFrame({'Similarity Run': similarity_means, 'Hybrid Run': hybrid_means})\n",
    "\n",
    "# difference between the means\n",
    "comparison_df['Difference'] = comparison_df['Similarity Run'] - comparison_df['Hybrid Run']\n",
    "\n",
    "# save dataframes to CSV files in the specified directory\n",
    "similarity_df.to_csv(os.path.join('similarity_run_data.csv'), index=False)\n",
    "hybrid_df.to_csv(os.path.join('hybrid_run_data.csv'), index=False)\n",
    "comparison_df.to_csv(os.path.join('comparison_data.csv'), index=True)\n",
    "\n",
    "print(\"Dataframes saved successfully in the local directory.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "SqzqmWy3w_Ik",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 186,
     "status": "ok",
     "timestamp": 1718831197677,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "SqzqmWy3w_Ik",
    "outputId": "c700cc45-7e51-41f6-aad2-e0ff8e9b06b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframes loaded successfully from the local directory.\n",
      "Performance Comparison:\n",
      "\n",
      "**Retrieval**:\n",
      "                   Similarity Run  Hybrid Run  Difference\n",
      "context_precision        0.906113    0.841267    0.064846\n",
      "context_recall           0.950000    0.925000    0.025000\n",
      "\n",
      "**Generation**:\n",
      "                  Similarity Run  Hybrid Run  Difference\n",
      "faithfulness            0.977500    0.945833    0.031667\n",
      "answer_relevancy        0.968222    0.965247    0.002976\n",
      "\n",
      "**End-to-end evaluation**:\n",
      "                    Similarity Run  Hybrid Run  Difference\n",
      "answer_correctness        0.776018    0.717365    0.058653\n",
      "answer_similarity         0.969899    0.969170    0.000729\n"
     ]
    }
   ],
   "source": [
    "### load dataframes from CSV files\n",
    "sem_df = pd.read_csv(os.path.join('similarity_run_data.csv'))\n",
    "rec_df = pd.read_csv(os.path.join('hybrid_run_data.csv'))\n",
    "comparison_df = pd.read_csv(os.path.join('comparison_data.csv'), index_col=0)\n",
    "\n",
    "print(\"Dataframes loaded successfully from the local directory.\")\n",
    "\n",
    "# Analysis that consolidates everything into easier to read scores\n",
    "print(\"Performance Comparison:\")\n",
    "print(\"\\n**Retrieval**:\")\n",
    "print(comparison_df.loc[['context_precision', 'context_recall']])\n",
    "print(\"\\n**Generation**:\")\n",
    "print(comparison_df.loc[['faithfulness', 'answer_relevancy']])\n",
    "print(\"\\n**End-to-end evaluation**:\")\n",
    "print(comparison_df.loc[['answer_correctness', 'answer_similarity']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "MFE4BY_rxN7W",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 1523,
     "status": "ok",
     "timestamp": 1718831430258,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": 240
    },
    "id": "MFE4BY_rxN7W",
    "outputId": "4d1dca08-dbdb-416a-f606-56c1e3e9a616"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting - create subplots for each category with increased spacing\n",
    "fig, axes = plt.subplots(3, 1, figsize=(12, 18), sharex=False)\n",
    "bar_width = 0.35\n",
    "categories = ['Retrieval', 'Generation', 'End-to-end evaluation']\n",
    "metrics = [\n",
    "    ['context_precision', 'context_recall'],\n",
    "    ['faithfulness', 'answer_relevancy'],\n",
    "    ['answer_correctness', 'answer_similarity']\n",
    "]\n",
    "\n",
    "# iterate over each category and plot the corresponding metrics\n",
    "for i, (category, metric_list) in enumerate(zip(categories, metrics)):\n",
    "    ax = axes[i]\n",
    "    x = range(len(metric_list))\n",
    "\n",
    "    # plot bars for Similarity Run (hex color #D51900)\n",
    "    similarity_bars = ax.bar(x, comparison_df.loc[metric_list, 'Similarity Run'], width=bar_width, label='Similarity Run', color='#D51900', hatch='///')\n",
    "\n",
    "    # add values to Similarity Run bars\n",
    "    for bar in similarity_bars:\n",
    "        height = bar.get_height()\n",
    "        ax.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.1%}', ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "    # plot bars for Hybrid Run (hex color #992111)\n",
    "    hybrid_bars = ax.bar([i + bar_width for i in x], comparison_df.loc[metric_list, 'Hybrid Run'], width=bar_width, label='Hybrid Run', color='#992111', hatch='\\\\\\\\\\\\')\n",
    "\n",
    "    # add values to Hybrid Run bars\n",
    "    for bar in hybrid_bars:\n",
    "        height = bar.get_height()\n",
    "        ax.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.1%}', ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "    ax.set_title(category, fontsize=14, pad=20)\n",
    "    ax.set_xticks([i + bar_width / 2 for i in x])\n",
    "    ax.set_xticklabels(metric_list, rotation=45, ha='right', fontsize=12)\n",
    "\n",
    "    # move the legend to the bottom right corner\n",
    "    ax.legend(fontsize=12, loc='lower right', bbox_to_anchor=(1, 1))\n",
    "\n",
    "# Add overall labels and title\n",
    "fig.text(0.04, 0.5, 'Scores', va='center', rotation='vertical', fontsize=14)\n",
    "fig.suptitle('Performance Comparison', fontsize=16)\n",
    "\n",
    "# adjust the spacing between subplots and increase the top margin\n",
    "plt.tight_layout(rect=[0.05, 0.03, 1, 0.95])\n",
    "plt.subplots_adjust(hspace=0.6, top=0.92)\n",
    "plt.show()"
   ]
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